On the Way of Applying Urgent Computing Solutions to Forest Fire Propagation Prediction

Abstract

A quick response becomes crucial in natural hazard management. When an emergency occurs, hazard evolution simulators are a very helpful tool for the teams in charge of making decisions. To perform the simulations, they rely on data which usually constitutes a big set of parameters, which have been previously recorded from observations, usually coming from remote sensors, images, etc. However, this data is frequently subject to a high degree of uncertainty. This data uncertainty also produces uncertainty in simulators’ results. To overcome this drawback, in previous works we developed a two-stage prediction method, which has been demonstrated to improve noticeably the quality of the predictions. The time incurred in performing this strategy, however, may vary significantly depending on different factors. As it is well known, the execution time of a particular simulator depends on the specific setting of the input parameters. Moreover, decision control centers in charge of making decisions to fight against the ongoing disaster require a certain degree of quality in the final prediction. Depending on how demanding are the quality and time constraints, the two-stage strategy may need the support of Urgent Computing solutions, in order to meet the requirements. In this work, we focus on forest fires spread prediction, as a real application case of study, and we expose our methodology to characterize both the time needed and the expected quality of the two-stage prediction method, so that we are able to determine the amount of computational resources needed to respond effciently to an eventual emergency. This way, we emphasize the need of Urgent Computing mechanisms to be able to put into practice this method at real time.